Needless to say much Recommendation systems, they are everywhere these days. Be it Amazon, Netflix, or YouTube, you will find yourself utilizing the goodness provided by an AI powered recommender system. Recommender system can be defined as a type of information filtering system that draws from huge data sets and create an impression for a user to generate recommendations for them. A quick evaluation recipe that you can rely upon to evaluate the deep learning-based recommendation system you train on Abacus.AI platform:
Compare metric scores with the baseline scores to see if there's an improvement or not
Check NDCG, personalization, and coverage, they should lie in the optimal ranges (NDCG - 0.15 to 0.40, Personalization - 0.4 or higher, and coverage - 0.01 to 0.2)
Using the prediction dashboard, compare the recommendations generated (on the right side of prediction dashboard) to the user's purchase history (list on the left side) for resemblance
Repeat step 4 for 5 to 10 users to make sure that the recommendations make sense
If all of the above things look good then the model is good to go and you have trained a world-class deep learning model-based recommendation model in just a few minutes with only a few clicks.